International Electronic Journal of Mathematics Education

International Electronic Journal of Mathematics Education Indexed in ESCI
Exploring Calculus I students’ performance between varying course times among other predictive variables
APA
In-text citation: (Hurdle et al., 2022)
Reference: Hurdle, Z. B., Akbuga, E., & Schrader, P. (2022). Exploring Calculus I students’ performance between varying course times among other predictive variables. International Electronic Journal of Mathematics Education, 17(4), em0700. https://doi.org/10.29333/iejme/12234
AMA
In-text citation: (1), (2), (3), etc.
Reference: Hurdle ZB, Akbuga E, Schrader P. Exploring Calculus I students’ performance between varying course times among other predictive variables. INT ELECT J MATH ED. 2022;17(4), em0700. https://doi.org/10.29333/iejme/12234
Chicago
In-text citation: (Hurdle et al., 2022)
Reference: Hurdle, Zachariah Benton, Enes Akbuga, and Paul Schrader. "Exploring Calculus I students’ performance between varying course times among other predictive variables". International Electronic Journal of Mathematics Education 2022 17 no. 4 (2022): em0700. https://doi.org/10.29333/iejme/12234
Harvard
In-text citation: (Hurdle et al., 2022)
Reference: Hurdle, Z. B., Akbuga, E., and Schrader, P. (2022). Exploring Calculus I students’ performance between varying course times among other predictive variables. International Electronic Journal of Mathematics Education, 17(4), em0700. https://doi.org/10.29333/iejme/12234
MLA
In-text citation: (Hurdle et al., 2022)
Reference: Hurdle, Zachariah Benton et al. "Exploring Calculus I students’ performance between varying course times among other predictive variables". International Electronic Journal of Mathematics Education, vol. 17, no. 4, 2022, em0700. https://doi.org/10.29333/iejme/12234
Vancouver
In-text citation: (1), (2), (3), etc.
Reference: Hurdle ZB, Akbuga E, Schrader P. Exploring Calculus I students’ performance between varying course times among other predictive variables. INT ELECT J MATH ED. 2022;17(4):em0700. https://doi.org/10.29333/iejme/12234

Abstract

This study focuses on the analysis of certain performance predictors for calculus I. We collected data from 717 students from 2013 through 2018 at a southeastern university in the United States to explore any correlation between course times (particularly very early versus the rest) and student performance in this specific course, along with a handful of other variables. This represented all calculus I students over this time period. A two-proportion test confirmed that time was a significant variable in performance. We then used regression to determine similar impacts of gender, major, instructor, and term on student performance. Initial findings portrayed statistical differences between terms and course times; other findings included the significance of major and instructor in different contexts. Interaction effects were used with time to complete our analysis of its impact, and controls were later used accordingly. We also display appropriate models for comparing categories. We conclude with some basic assertions and argue some departmental recommendations on how to use these findings in undergraduate mathematics education.

References

  • Aiken-Wisniewski, S. A., Smith, J. S., & Troxel, W. G. (2010). Expanding research in academic advising: Methodological strategies to engage advisors in research. NACADA Journal, 30(1), 4-13. https://doi.org/10.12930/0271-9517-30.1.4
  • Akbuga, E. (2018). Motivation intervention through calculus tasks with science and engineering applications. https://digital.library.txstate.edu/handle/10877/7413
  • Akbuga, E., Hurdle, Z., Daniel, S., & Laffey, R. (2019). Using calculus writing assignments to foster student motivation. Mathematics in School, 47(4), 37-39.
  • Berry, J. S., & Nyman, M. A. (2003). Promoting students’ graphical understanding of the calculus. The Journal of Mathematical Behavior, 22(4), 479-495. https://doi.org/10.1016/j.jmathb.2003.09.006
  • Bloemer, W., Day, S., & Swan, K. (2017). Gap analysis: An innovative look at gateway courses and student retention. Online Learning, 21(3), 5-14. https://doi.org/10.24059/olj.v21i3.1233
  • Bressoud, D. M., Carlson, M. P., Mesa, V., & Rasmussen, C. (2013). The calculus student: Insights from the Mathematical Association of America national study. International Journal of Mathematical Education in Science and Technology, 44(5), 685-698. https://doi.org/10.1080/0020739X.2013.798874
  • Bressoud, D. M., Mesa, V., & Rasmussen, C. L. (2015). Insights and recommendations from the MAA national study of college calculus. https://doi.org/10.5951/mathteacher.109.3.0178
  • Bridgeman, B., & Lewis, C. (1996). Gender differences in college mathematics grades and SAT-M scores: A reanalysis of Weiner and Steinberg. Journal of Educational Measurement, 33(3), 257-270. https://doi.org/10.1111/j.1745-3984.1996.tb00492.x
  • Carrell, S. E., Maghakian, T., & West, J. E. (2011). A’s from Zzzz’s? The causal effect of school start time on the academic achievement of adolescents. American Economic Journal: Economic Policy, 3(3), 62-81. https://doi.org/10.1257/pol.3.3.62
  • Ellis, J., Fosdick, B. K., & Rasmussen, C. (2016). Women 1.5 more likely to leave STEM pipeline after calculus compared to men: Lack of mathematical confidence a potential culprit. PLOS One, 11(7), 1-14. https://doi.org/10.1371/journal.pone.0157447
  • Eskew, R. W. (2013). Trends in grade distributions at Hilbert College: Report to the Provost’s Council. https://www.hilbert.edu/docs/cdefault-source/Academics/Institutional-Research-Assessment/oira_report-on-grade-distributions_2009_12.pdf?sfvrsn=8e29bc3f_0
  • Hagman, J. E., Johnson, E., & Fosdick, B. K. (2017). Factors contributing to students and instructors experiencing a lack of time in college calculus. International Journal of STEM Education, 4(12), 1-15. https://doi.org/10.1186/s40594-017-0070-7
  • Hall, M., Smith, K., Boeckman, D., Ramachandra, V., & Jasin, J. (2003). Why do students withdraw from courses? In Proceedings of Southern Association for Institutional Research Conference. San Antonio, TX, USA.
  • Hurdle, Z. & Mogilski, W. (2022). The impact of prerequisites for undergraduate Calculus I performance. International Electronic Journal of Mathematics Education, 17(3), em0696. https://doi.org/10.29333/iejme/12146
  • Koch, A. K., & Pristilli, M. D. (2014). Analystics and gateway courses: Understanding and overcoming roadblocks to college completion. John Gardner Institute.
  • Onyper, S. V., Thacher, P. V., Gilbert, J. W., & Gradess, S. G. (2012). Class start times, sleep, and academic performance in college: A path analysis. Chronobiology International, 29(3), 318-335. https://doi.org/10.3109/07420528.2012.655868
  • Pope, N. G. (2016). How the time of day affects productivity: Evidence from school schedules. The Review of Economics and Statistics, 98(1), 1-11. https://doi.org/10.1162/REST_a_00525
  • Wahistrom, K. (2002). Changing times: Findings from the first longitudinal study of later high school start times. NASSP Bulletin, 86(633), 3-21. https://doi.org/10.1177/019263650208663302
  • Webb, D. (2016). Appling principles for active learning to promote student engagement in undergraduate calculus. In Proceedings of the 13th International Congress on Mathematical Education.Hamburg, Germany.
  • Wheaton, A. G., Chapman, D. P., & Croft, J. B. (2016). School start times, sleep, behavioral, health, and academic outcomes: A review of the literature. The Journal of School Health, 86(5), 363-381. https://doi.org/10.1111/josh.12388
  • White, P., & Mitchelmore, M. (1996). Conceptual knowledge in introductory calculus. Journal for Research in Mathematics Education, 27(1), 79-95. https://doi.org/10.2307/749199
  • Wu, X., Deshler, J., & Fuller, E. (2018). The effects of different versions of a gateway STEM course on student attitudes and beliefs. International Journal of STEM Education, 5(1), 44. https://doi.org/10.1186/s40594-018-0141-4
  • Young-Jones, A. D., Burt, T. D., Dixon, S., & Hawthorne, M. J. (2013). Academic advising: Does it really impact student success? Quality Assurance in Education, 21(1), 7-19. https://doi.org/10.1108/09684881311293034

License

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.